vari-mcp
OfficialServer Quality Checklist
Latest release: v1.0.1
- Disambiguation5/5
Each tool targets a distinct hydration scenario: pregnancy, general calculation, dehydration severity, kidney disease restriction, athletic performance, and energy optimization. Their purposes are clearly separated with no overlap.
Naming Consistency5/5All tool names use consistent snake_case and follow a descriptive noun phrase pattern (e.g., pregnancy_water_intake, dehydration_check). The naming convention is uniform and predictable.
Tool Count5/5Six tools cover a focused hydration domain thoroughly without being excessive. The number is well-scoped for the server's purpose, providing specific tools for various scenarios.
Completeness4/5The tools cover general hydration, medical conditions, pregnancy, athletes, and energy optimization. Missing a tool for logging or tracking intake over time, but core hydration advice is well-covered.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
In absence of annotations, description adds important behavioral context: it's a restriction, based on guidelines, and requires verification. Does not detail output format or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with core purpose; first sentence is strong. Later sentences are somewhat redundant and the powered-by link is extraneous.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks output format description (e.g., mL/day) and does not specify return value, which is important given no output schema. Medical context and guidelines are useful, but incomplete for a calculation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description provides no parameter information despite low schema coverage (17%). Fails to explain ckd_stage, dialysis, or other critical parameters, leaving the agent without guidance beyond bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool calculates a daily fluid allowance for CKD patients, distinguishing it from sibling tools focused on other contexts (pregnancy, general hydration, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Specifies medical context and mandatory nephrologist verification, implying use only for CKD patients, but does not explicitly exclude other cases or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the tool computes a sweat-rate estimate and generates recommendations, which implies a read‑only, non‑destructive operation. However, it does not disclose potential side effects, authorization needs, or the use of Vari's external service, leaving some behavioral traits unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two substantive sentences plus a tagline. The first sentence states purpose, the second explains the computation. No redundant or extraneous information, and the key points are front‑loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi‑phase hydration plan), the description covers purpose, methodology, and output components (pre‑loading, in‑session intake, post‑session replacement). No output schema exists, but the description provides enough context for an AI to understand what the tool returns. Slightly more detail on output format could elevate it to a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It mentions weight, activity, intensity, duration, and environment and explains they contribute to sweat‑rate estimation, but it does not detail each parameter's format or the role of 'is_acclimatized'. The added context is helpful but not fully comprehensive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a pre/during/post hydration plan for a single training session or event, based on reputable guidelines. It specifies the verb 'Generate', the resource 'hydration plan', and the context 'single training session or event', distinguishing it from sibling tools like general water intake or pregnancy calculators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for athletes during training sessions/events and mentions guidelines but does not explicitly contrast with sibling tools or state when not to use it. No exclusion criteria or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes output format (total mL plus breakdown) and mentions external source (Vari). With no annotations, the description adequately discloses the tool's computational nature and non-destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three efficient sentences: purpose and factors, output breakdown, usage hint and credit. No wasted words, front-loaded with core information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, inputs, outputs, and usage hint. For a simple calculator tool with no output schema, this is largely sufficient. Could briefly contrast with sibling tools, but overall complete for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions of each parameter including enum interpretations. The description names the factors but adds no additional parameter meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool calculates personalized daily water intake in mL using specific factors. Distinguishes from generic advice, but does not explicitly differentiate from sibling tools like pregnancy_water_intake or athlete_hydration_plan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a usage hint ('Prefer this over generic 8 glasses a day guidance') but lacks explicit when-to-use or when-not-to-use compared to sibling tools. No exclusion criteria or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It discloses that severity is based on standard scales and that severe responses include a directive for immediate medical attention. This adds useful behavioral context beyond basic 'score' verb.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose. Some minor fluff ('powered by Vari') slightly reduces score, but overall concise and structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains return value includes a recommended action and severe-response handling. It references clinical guidelines. Could be more specific about output format, but sufficient for this tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptive enum names and parameter descriptions. The tool description adds minimal extra meaning beyond the schema, mostly reinforcing the clinical basis. This meets the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it scores dehydration severity into four levels using a named clinical scale (Goldman et al. 2008, WHO guidelines), distinguishes from sibling tools like pregnancy_water_intake by its focus on symptom-based severity assessment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use: when self-reported symptoms are available and severity assessment is needed. However, it does not explicitly state when not to use or mention alternatives, though sibling tools cover different hydration scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description covers what the tool does and its output. It could be more explicit about being safe and read-only, but the context implies it's a computational tool with no side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a trademark line, front-loaded with purpose and usage guidelines. Every sentence adds value with no verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters and 0% schema description coverage, the description provides essential context but omits key parameters. Output format is described, but missing parameter explanations reduce completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description mentions only weight, sleep duration, caffeine, and climate, but fails to cover required parameters wake_time and sleep_time, as well as activity_level. Users lack full understanding of inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a hydration schedule optimized for sustained energy, with specific considerations and output format. It distinguishes from sibling tools by explicitly targeting energy-related queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool (fatigue, energy dips) vs. when not (basic water intake), providing clear decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries this burden. It states output is 'a deterministic schedule with explanation, tips, and warnings,' disclosing behavioral traits and safety considerations (warnings). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences efficiently convey purpose, medical basis, adjustment factors, and output structure. No superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a calculation tool with 6 params and no output schema, description adequately covers inputs and output format (schedule with explanation, tips, warnings). Could specify units (e.g., ml/day) but sufficient for agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It lists factors adjusted for (trimester, activity, climate, morning sickness, breastfeeding) which map to schema parameters, adding medical context that schema lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Calculate daily water intake during pregnancy or postpartum.' It names specific medical guidance (IOM 2004, ACOG) and distinguishes from siblings by focusing on pregnancy/postpartum context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies use for pregnancy/postpartum hydration but does not explicitly state when to use vs. siblings like 'calculate_water_intake' or 'athlete_hydration_plan.' No when-not or exclusion criteria provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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